Test of labor-extensive methods to monitor floodplain vegetation and water quality of floodplain waterbodies.
Riverine floodplains are important for hydrodynamics, ecology, and socio-economics. This variety of functions can be assessed within the framework of ecosystem services. Some fluvial ecosystem services can be computed based on landscape ecological units, such as ecotopes. However, the ecosystem services vary with spatial and temporal scale, and should be documented properly documented and mapped. Currently, there is no ecotope classification at a comprehensive multi-temporal scale and no multi-scale monitoring methods exist that can automate mapping of ecotopes. Further need for monitoring arises from the flood reduction measures that are currently implemented, which lead to a high spatio-temporal variability of the ecotope distribution. From the perspective of the current river management, our methods will provide an integrated, multiscale monitoring toolbox which will serve river managers and planners alike.
Key goals: Innovative monitoring
Examples of the different data types used to monitor vegetation in this study. Study area is Breemwaard floodplain along the River Waal.
The main scientific challenge is to determine a multiscale ecotope classification that can be linked to the fluvial ecosystem services, and that can be monitored using remote sensing data. The aim of this research is:
- To explore different types of remote sensing data acquired at various spatial scales and from different platforms, ranging from mobile terrestrial, via airborne, to spaceborne.
- To explore temporal aspects of vegetation development, such as phenology and succession to be included in the ecotope classification.
For whom and where?
Vegetation management departments of water management organisations in the Netherlands and internationally.
Data-collection methods: Field survey measurements Remote sensing
Temporal scale: Seasonal measurements
Application development and findings
A multi-temporal field study in the Breemwaard floodplain uses unmanned airborne vehicle (UAV) imagery to obtain accurate temporal height profiles of low floodplain vegetation over the growing season. To up-scale the region of interest to a river reach of 100+ km, we use data from spaceborne platforms. We focus on high spatial resolution multispectral data and synthetic aperture radar (SAR) data. We are collecting data from multi-parameter stations along the River IJssel to relate existing measurements at gauging stations along the Rhine branches with local continuous monitoring of abiotic conditions of floodplain waterbodies.
Status for day-to-day practice
The usage of multi-temporal data from UAVs greatly improved classification accuracies of grassland and herbaceous vegetation classes in floodplains. Input data contributing most to the classification accuracy are the spectral and vegetation height layers covering the extremes of the vegetation phenology. Most classification errors occurred in classes without seasonal changes, such as water and paved surfaces. In addition, confusion between natural and production grassland remained. This may be resolved by using thematic data on management of these grassland areas, but preferably this may be resolved by fine-tuning the classification method. However, monitoring the floodplains large catchments with UAV imagery is an impossible task. Therefore, the main next step will be to up-scale the region of interest to a river reach of 100+ km.
Spatial scale: Floodplain
Key locations: IJssel River (NL) Waal River (NL)
This section will be available as soon as possible.
Last modified: 31/01/2019
Explore the contact details to get to know more about the researchers, the supervisory team and the organizations that contribute to this project.
Wimala van Iersel
As soon as available, explore the storyline to get to know more about the main methods or prototype tools that were developed within this project.
Explore the output details for available publications to get a glance of the innovative components and implications to practice as well as the links to supporting datasets.
Jump to: Conference abstract |
- Collas, F.P.L., van Iersel, W., Straatsma, M. and Leuven, R.S.E.W. (2018). Modelling effects of spatiotemporal temperature variation on alien and native fish species in riverine ecosystems using thermal imagery. In: Huismans, Y., Berends, K.D., Niesten, I., Mosselman, E (Eds.). The future river: NCR DAYS 2018 Proceedings. Netherland Centre for River Studies publication 42-2018, 8-9 February 2018, Deltares, Delft, pp. 54-55.
- Van Iersel, W. K., Straatsma, M. W., Addink, E. A., & Middelkoop, H. (2017). Monitoring vegetation height and greenness of low floodplain vegetation using UAV-remote sensing . In: S. Lanzoni, M. Redolfi, G. Zolezzi (Eds.), RCEM 2017 – Back to Italy, 15-22 September, 2017, Padova, Italy, p. 48.
- Van Iersel, W.K., Addink, E.A., Straatsma, M.W., Middelkoop, H. (2016). Monitoring phenology of floodplain grassland and herbaceous vegetation with UAV imagery. In: L. Halounova, F. Sunar, M. Potůčková (Eds.). XXIII International Society for Photogrammetry and Remote Sensing (ISPRS) Congress, Commission VII, 12–19 July 2016, Prague, Czech Republic, p. 569-571 3 p. (International archives of the photogrammetry, remote sensing and spatial information sciences; vol. XLI-B7).
- Van Iersel, W.K., Addink, E.A., Straatsma, M.W., Middelkoop, H. (2016). River floodplain vegetation classification using multi-temporal high-resolution colour infrared UAV imagery GEOBIA 2016 : Solutions and Synergies. University of Twente Faculty of Geo-Information and Earth Observation (ITC).
Best poster award of Nederlands Aardwetenschappelijk congres April 7-8 2016.
- Van Iersel, W.K., Addink, E.A., Straatsma, M.W., Middelkoop, H. (2015). Monitoring vegetation height of grassland and herbaceous vegetation with remote sensing. In: H.J.R. Lenders, F.P.L. Collas, G.W. Geerling, R.S.E.W. Leuven (Eds.). Bridging gaps between river science, governance and management. Book of abstracts NCR-days 2015, NCR Publication 39-2015, 1-2 October 2015, Radboud University, Nijmegen, pp. 123-126.
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